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Chengliang Wang; Xiaojiao Chen; Zhebing Hu; Sheng Jin; Xiaoqing Gu – Journal of Computer Assisted Learning, 2025
Background: ChatGPT, as a cutting-edge technology in education, is set to significantly transform the educational landscape, raising concerns about technological ethics and educational equity. Existing studies have not fully explored learners' intentions to adopt artificial intelligence generated content (AIGC) technology, highlighting the need…
Descriptors: College Students, Student Attitudes, Computer Attitudes, Computer Uses in Education
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Suzhen Duan; Marisa Exter; Qing Li – TechTrends: Linking Research and Practice to Improve Learning, 2024
Preservice teachers' beliefs regarding technology integration significantly influence their future teaching practices. This qualitative study examines the beliefs and intentions of 51 preservice teachers within the context of technology integration in their envisioned teaching scenarios. Thematic analysis identified three primary themes. Firstly,…
Descriptors: Preservice Teachers, Student Attitudes, Beliefs, Technology Integration
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Laurie O. Campbell; Caitlin Frawley – Educational Technology Research and Development, 2024
Higher education faculty members incorporate technologies into their teaching and learning practices in higher education for the benefit of their learners. Hence, general technologies, such as presentation software, online classrooms, and learning management systems are ubiquitous in higher education teaching practices. However, emerging…
Descriptors: College Faculty, Intention, Technology Integration, Educational Technology
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Hsi-Hsun Yang – International Review of Research in Open and Distributed Learning, 2024
This study proposes a hypothetical model combining the unified theory of acceptance and use of technology (UTAUT) with self-determination theory (SDT) to explore design professionals' behavioral intentions to use artificial intelligence (AI) tools. Moreover, it incorporates job replacement (JR) as a moderating role. Chinese-speaking design…
Descriptors: Artificial Intelligence, Design, Intention, Models
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Jiaming Cheng; Jacob A. Hall; Qiu Wang; Jing Lei – Education and Information Technologies, 2024
Using pre-service teachers' (PSTs) technological, pedagogical, content knowledge (TPACK) survey responses, this study's cluster analysis identified five distinct learning profiles: Pedagogical Content Knowledge Specialists, Technological Forerunners, Pedagogically Minded, Balanced Integrators, and TPACK Lingerers. Instead of using a single…
Descriptors: Preservice Teachers, Teacher Education, Technology Uses in Education, Educational Technology
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Mussa Saidi Abubakari; Gamal Abdul Nasir Zakaria; Juraidah Musa – Cogent Education, 2024
Various factors, including technical, organisational, cultural, and individual, can influence how people adopt digital technologies (DT). However, different contexts have produced similar yet distinct results when researchers integrated these various factors into the technology acceptance model (TAM). Two critical factors in the Islamic…
Descriptors: Foreign Countries, Higher Education, Islam, Religious Education
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Thinley Wangdi; Karma Sonam Rigdel – Journal of Education for Teaching: International Research and Pedagogy, 2025
This qualitative study explored the factors that influence teachers' behavioural intention (BI) to use ChatGPT for teaching. The data was collected from 214 Bhutanese teachers using open-ended questions and interviews. The thematic analysis revealed four key factors that are likely to influence teachers' BI to use ChatGPT in the context. These…
Descriptors: Teacher Attitudes, Intention, Artificial Intelligence, Computer Software
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Kaspul Anwar; Juraidah Musa; Sallimah M. Salleh – Education and Information Technologies, 2025
This study examines the factors influencing preservice teachers' (PSTs) technology integration during teaching practice in teacher preparation programs. Utilizing a multidimensional framework, the study integrates models such as TPACK, UTAUT, and the Triple-E evaluation rubric, among others. The research involved a Qualtrics survey of 1,217 PSTs…
Descriptors: Preservice Teachers, Technology Uses in Education, Technology Integration, Preservice Teacher Education
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Caleb Or – OTESSA Journal, 2024
This study uses one-step meta-analytic structural equation modelling to delve into the technology acceptance model's (TAM) application within education, assessing perceived usefulness, ease of use, intentions to use, and actual technology use. It synthesises previous findings to validate the TAM's effectiveness and uncover the model's predictive…
Descriptors: Literature Reviews, Meta Analysis, Technology Integration, Educational Technology
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Wang, Qiong; Zhao, Guoqing – British Journal of Educational Technology, 2023
Technostress is an undesired consequence of information and communication technology (ICT) use and might negatively affect teachers. However, there is a lack of empirical research exploring the influences of technostress creators on teachers' ICT use. This study aims to bridge this gap by exploring the structural relationship among five…
Descriptors: Stress Variables, Educational Technology, Technology Integration, Teacher Attitudes
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Lisana, Lisana – Education and Information Technologies, 2023
Adopting technology by its intended users is one of the most important contributors to that technology's success. Therefore, the success of mobile learning (ML) depends on the students' acceptance of the method. Regarding this point, this quantitative research aims to identify factors that affect switching intention to adopt ML among university…
Descriptors: Handheld Devices, Telecommunications, Technology Integration, Intention
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Guillén-Gámez, Francisco D.; Ruiz-Palmero, Julio; García, Melchor Gómez – Education and Information Technologies, 2023
All spheres of our life are being affected using technology, particularly its integration in the research processes carried out by teachers. The success of the integration of specific digital resources in research work can be affected by several factors, such as: digital skills for finding information, managing it, analyzing it, and communicating…
Descriptors: Technological Literacy, Teacher Competencies, Teacher Researchers, Educational Technology
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Sasha Nikolic; Isabelle Wentworth; Lynn Sheridan; Simon Moss; Elisabeth Duursma; Rachel A. Jones; Montserrat Ros; Rebekkah Middleton – Australasian Journal of Educational Technology, 2024
The rapid advancement of artificial intelligence (AI) has outpaced existing research and regulatory frameworks in higher education, leading to varied institutional responses. Although some educators and institutions have embraced AI and generative AI (GenAI), other individuals remain cautious. This systematic literature review explored teaching…
Descriptors: College Faculty, Teacher Attitudes, Intention, Teacher Behavior
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Di Wu; Xinyan Zhang; Kaili Wang; Longkai Wu; Wei Yang – Educational Technology Research and Development, 2025
Artificial Intelligence (AI) is driving ecological shifts and systemic reforms in education. As practitioners of educational reform, teachers' behavioral intention to experience and accept the effectiveness of AI technologies will affect the quality of educational change. From an educational ecology perspective, this study explores the impact of…
Descriptors: Teacher Behavior, Intention, Technology Uses in Education, Educational Technology
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Yuan-Hsuan Lee; Huang-Yao Hong – Interactive Learning Environments, 2024
Integrating Information and Communication Technologies (ICT) for meaningful constructivist instruction has become essential in teacher education. This study investigated preservice teachers' intention to integrate ICT for constructivist learning from the perspectives of their Internet epistemic beliefs (IEB) and Internet-based learning…
Descriptors: Preservice Teachers, Information Technology, Technology Integration, Constructivism (Learning)
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